Database workload capture and replay

ABSTRACT

Technologies are described for facilitating data workload capture, workload replay, and comparing the performance of a plurality of database systems. A plurality of requests for database operations are received from one or more database clients. The requests for database operations are executed. For the plurality of requests for database operations, execution context information is generated for a plurality of execution contexts. For the plurality of requests for database operations, a plurality of performance measures are generated. The execution context information and the performance measures are stored in a workload capture store, such as a file. The workload capture file is useable to replay the captured workload at a second database system using at least a portion of the execution context information and at least a portion of the performance measures.

FIELD

The present disclosure generally relates to capturing and replaying a database workload. Particular implementations relate to comparing performance measures associated with the database workload under different execution conditions, such as at first and second database systems.

BACKGROUND

It is typically desirable to optimize the performance of a database system. Changing operational parameters of the database system, or changing to a new version of software implementing the database system, can, in some cases, have a negative affect on the processing speed or resource use of the database system. Before changing database system parameters or software, it can be useful to evaluate the performance of a test database system, such as to compare its performance with a production database system. Typically, a simulated or emulated workload is run on the test system. However, the simulated or emulated workload may not accurately reflect the workload experienced by the production database system. Accordingly, results from the test system may not accurately reflect the performance of the production database system under the changed parameters or software.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Techniques and solutions are described for capturing a workload at a database system, replaying a captured workload at a database system, and comparing the performance of database systems. In one embodiment, a plurality of requests for database operations, such as query language statements, are received by a database system from one or more database clients. The requests are executed at the database system. For each of the plurality of requests for database operations, execution context information is generated for a plurality of execution contexts. For each of the plurality of requests for database operations, a plurality of performance measures are generated. The execution context information and performance measures are stored, such as in execution context files and one or more performance measures files, in a workload capture store, such as in a workload capture file. In particular examples, the context information and performance measures are stored in a schema, such as a star schema.

In some implementations, identifiers, such as hash values, can be generated for at least portion of the execution context information or the performance measures. The identifiers can be used, for example, to compare execution results of the database system with the results of executing the requests for database operations when replayed on another database system.

The workload capture store can include additional information, such as events from applications interfacing with the database system, or from internal database system events. In some cases, filter criteria, such as user-defined filter criteria, can be used to determine information to be included in the workload capture store. For example, the user can select a portion of the requests for database operations to be captured.

In some cases the database system can be a distributed database system, and the workload capture store can include, for at least a portion of the requests for database operations, an identifier for a node or server of the database system which executed all or a portion of a request for a database operation. When a request for a database operation generates nondeterministic values, in particular examples, the workload capture store can include the nondeterministic values to be used when the corresponding request for a database operation is replayed at another database system.

In another embodiment, the present disclosure relates to producing workload replay data from workload capture data. Workload capture data, such as in a workload capture store, for example, a workload capture file, is received. The workload capture data includes stores, such as files, for each of a plurality of execution contexts and one or more stores, such as files, for performance measures. The plurality of execution context stores and one or more performance measure stores are extracted to provide execution context data and performance measure data. For each of a plurality of workload capture units, such as database sessions, execution context data and performance measure data associated with the respective workload capture unit are collected. For each of the workload capture units, the collected execution context data and performance measure data are stored in a format replayable by another database system.

In particular examples, one or more of the workload capture store, execution context stores, and one or more performance measure stores are compressed. Extracting execution context stores and one or more performance measure stores can include decompressing the workload capture store. The execution context stores and one or more performance measure stores can also be decompressed. In some cases, one or more of the extracting, collecting, and storing can occur in parallel for different workload capture units.

In a further embodiment, the present disclosure relates to replaying captured workload information, such as to compare the performance of database systems. Workload replay data associated with a workload captured at a first database system is received. The workload replay data includes one or more requests for database operations and first performance data associated with the one or more requests. The one or more requests for database operations are executed at a second database system. Second performance data associated with the execution of the one or more requests for database operations at the second database system is generated. The first and second performance data are compared.

In some implementations, execution results associated with executing the one or more requests for database operations can be compared with execution results associated with executing the one or more requests for database operations at the first database system. For example, identifiers, such as hash values, can be compared.

In some aspects, the first database system can be distributed among a plurality of servers or nodes. The workload replay data can include an identifier for a node of the first database system involved in executing a particular request for a database operation. The request for a database operation and identifier can be sent to a first node of the second database system. The first node can send an identifier of a second node of the second database system to execute the request. The request for a database operation can be sent to the second node of the second database system, and execution results received from the second node of the second database system.

In a further aspect, the second database system can include a database image associated with a first time. Each of the one or more requests for database operations can be associated with a time. The time of the one or more requests for database operations can be compared with the database image time, and replayed if the time associated with a request for a database operation is greater than the time associated with the database image.

The present disclosure also includes computing systems and tangible, non-transitory computer readable storage media configure to carry out, or including instructions for carrying out, an above-described method. As described herein, a variety of other features and advantages can be incorporated into the technologies as desired.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram depicting a first database environment having a first database system and a second database environment having a second database system executing an emulated workload of the first database system.

FIG. 2 is a diagram depicting a database environment providing for processing of requests for database operations.

FIG. 3 is a diagram illustrating database environments for capturing a database workload at a first database system and replaying the workload at a second database system.

FIG. 4 is a diagram of a workload capture file schema for storing execution context data and performance data.

FIG. 5 is a block diagram of an example software architecture for implementing workload capture according to an embodiment of the present disclosure.

FIG. 6 is a diagram depicting storing, such as writing to a plurality of files, of buffered workload capture data according to an embodiment of the present disclosure.

FIG. 7 is a diagram illustrating a method for comparing the performance of a first database system with a second database system.

FIG. 8 is a diagram depicting an OLAP cube of workload report useable to compare the performance of a first database system with a second database system according to an embodiment of the present disclosure.

FIG. 9 is a flowchart of an example method for capturing workload information at a database system.

FIG. 10 is a flowchart of an example method for preparing workload replay data from captured workload data.

FIG. 11 is a flowchart of an example method for replaying a workload at a database system.

FIG. 12 is a diagram illustrating the execution of transactions at a database system while an image of the database system is being acquired and a workload capture process is being carried out.

FIG. 13 is a flowchart of an example method for determining whether a request for a database operation should be replayed at a database system.

FIG. 14 is a diagram of a database environment for acquiring hash values of execution results associated with the execution of requests for database operations at a database system.

FIG. 15 is diagram of a database environment for comparing hash values of execution results of workload replay at a second database system with hash values of execution results of workload execution at a first database system.

FIG. 16A is a flowchart of an example method for acquiring hash values for execution results of workload execution at a first database system.

FIG. 16B is a flowchart of an example method for comparing hash values of execution results of workload replay at a second database system with hash values of execution results of a workload executed at a first database system.

FIG. 17 is a diagram of a database environment for storing database system routing information at a database client.

FIG. 18 is a diagram of a database environment for storing routing information associated with requests for database operations captured at a first database system and replaying the requests for database operations at a second database system.

FIG. 19 a flowchart of a method for replaying requests for database operations captured from a first distributed database system at a second database system.

FIG. 20 is an example UI screen for initiating a workload capture, including selection of workload capture filter criteria.

FIG. 21 is a flowchart of an example method for capturing requests for database operations meeting filter criteria in a database workload.

FIG. 22 is a diagram of a process for incrementally converting a database workload capture file into a format replayable at a database system.

FIG. 23 is a flowchart of an example method for incrementally converting a workload capture file into request data and parameter data replayable at a database system.

FIG. 24 is a diagram of an example computing system in which some described embodiments can be implemented.

FIG. 25 is an example cloud computing environment that can be used in conjunction with the technologies described herein.

DETAILED DESCRIPTION Example 1 Overview

It is often of interest to optimize the processing of database operations. Database systems commonly operate using online transaction processing (OLTP) workloads, which are typically transaction-oriented, or online analytical processing (OLAP) workloads, which typically involve data analysis. OLTP transactions are commonly used for core business functions, such as entering, manipulating, or retrieving operational data, and users typically expect transactions or queries to be completed quickly. For example, OLTP transactions can include operations such as INSERT, UPDATE, and DELETE, and comparatively simple queries. OLAP workloads typically involve queries used for enterprise resource planning and other types of business intelligence. OLAP workloads commonly perform few, if any, updates to database records, rather, they typically read and analyze past transactions, often in large numbers. Because OLAP processes can involve complex analysis of a large number of records, they can require significant processing time.

Timely processing of OLTP workloads is important, as they can directly affect business operation and performance. However, timely processing of OLAP workloads is also important, as even relatively small improvements can result in significant time savings.

The programs responsible for implementing a database system are typically periodically updated. In addition, users, such as database administrators, may wish to change various database parameters in order to determine whether such changes may improve database performance.

Migrating a database system to a new program version, or seeking to optimize database operational parameters, can be problematic. For example, for a production (currently in operational use) database system, parameter or software version changes may negatively affect the usability, stability, or speed of the database system. Users may seek to create a test database system in order to evaluate the performance impact of using a new program version, or changing the parameters of a new or existing program version, in order to avoid negative impacts on a production database system.

In at least some embodiments, a workload refers to an amount of work, such as work involving data transfer or processing at a database system, over time. The workload can include requests for database operations received by the database system from database clients. The workload can also include internal database operations, such as transferring or copying information in memory to persistent storage, the generation of temporary tables or other data (including data or metadata associated with a request for a database operation), and incorporating of temporary or other data into primary data sources.

FIG. 1 illustrates a database environment 100 having a first, source database environment 110 that includes one or more clients 115, one or more applications servers 120 available to service requests for database operations from the clients, and a first database system 125 on which the database operations are carried out. The database environment 100 also includes a second, test database environment 130 having an emulated workload 135, such as a workload that seeks to replicate a workload produced by the clients 115 of the first database environment 110. The second database environment 130 includes application servers 140 to service requests for database operations from the emulated workload 135. The database operations are carried out on a second database system 145, such as a database system 145 having different operational parameters or a different software version than the first database system 125.

Testing the performance of the second database system 145 under a workload at least similar to that experienced by the first database system 125 can be problematic. Typically, a test database system is evaluated using an artificially generated workload, such as the emulated workload 135. However, these artificial workloads may not accurately reflect the actual workloads experienced by the first, production database system 125. Thus, predicted negative or positive performance impacts observed on the second database system 145 may not accurately reflect performance under a workload experienced by the first database system 125.

Capturing a workload from the first database environment 110 to run at the second database environment 130 can also be problematic. For example, it may be difficult to capture all the inputs necessary to replicate the workload generated by the clients 115. In addition, the capture process itself may negatively impact the performance of the first database system 125, such as by increasing the processing load on a computing system operating the database system, or delaying processing of operations on the first database system 105.

FIG. 2 illustrates an example database environment 200. The database environment 200 can include a client 204. Although a single client 204 is shown, the client 204 can represent multiple clients. The client or clients 204 may be OLAP clients, OLTP clients, or a combination thereof.

The client 204 is in communication with a database server 206. Through various subcomponents, the database server 206 can process requests for database operations, such as requests to store, read, or manipulate data. A session manager component 208 can be responsible for managing connections between the client 204 and the database server 206, such as clients communicating with the database server using a database programming interface, such as Java Database Connectivity (JDBC), Open Database Connectivity (ODBC), or Database Shared Library (DBSL). Typically, the session manager 208 can simultaneously manage connections with multiple clients 204. The session manager 208 can carry out functions such as creating a new session for a client request, assigning a client request to an existing session, and authenticating access to the database server 206. For each session, the session manager 208 can maintain a context that stores a set of parameters related to the session, such as settings related to committing database transactions or the transaction isolation level (such as statement level isolation or transaction level isolation).

For other types of clients 204, such as web-based clients (such as a client using the HTTP protocol or a similar transport protocol), the client can interface with an application manager component 210. Although shown as a component of the database server 206, in other implementations, the application manager 210 can be located outside of, but in communication with, the database server 206. The application manager 210 can initiate new database sessions with the database server 206, and carry out other functions, in a similar manner to the session manager 208.

The application manager 210 can determine the type of application making a request for a database operation and mediate execution of the request at the database server 206, such as by invoking or executing procedure calls, generating query language statements, or converting data between formats useable by the client 204 and the database server 206. In particular examples, the application manager 210 receives requests for database operations from a client 204, but does not store information, such as state information, related to the requests.

Once a connection is established between the client 204 and the database server 206, including when established through the application manager 210, execution of client requests is usually carried out using a query language, such as the structured query language (SQL). In executing the request, the session manager 208 and application manager 210 may communicate with a query interface 212. The query interface 212 can be responsible for creating connections with appropriate execution components of the database server 206. The query interface 212 can also be responsible for determining whether a request is associated with a previously cached statement or a stored procedure, and calling the stored procedure or associating the previously cached statement with the request.

At least certain types of requests for database operations, such as statements in a query language to write data or manipulate data, can be associated with a transaction context. In at least some implementations, each new session can be assigned to a transaction. Transactions can be managed by a transaction manager component 214. The transaction manager component 214 can be responsible for operations such as coordinating transactions, managing transaction isolation, tracking running and closed transactions, and managing the commit or rollback of transactions. In carrying out these operations, the transaction manager 214 can communicate with other components of the database server 206.

The query interface 212 can communicate with a query language processor 216, such as a structured query language processor. For example, the query interface 212 may forward to the query language processor 216 query language statements or other database operation requests from the client 204. The query language processor 216 can include a query language executor 220, such as a SQL executor, which can include a thread pool 224. Some requests for database operations, or components thereof, can be executed directly by the query language processor 216. Other requests, or components thereof, can be forwarded by the query language processor 216 to another component of the database server 206. For example, transaction control statements (such as commit or rollback operations) can be forwarded by the query language processor 216 to the transaction manager 214. In at least some cases, the query language processor 216 is responsible for carrying out operations that manipulate data (e.g., SELECT, UPDATE, DELETE). Other types of operations, such as queries, can be sent by the query language processor 216 to other components of the database server 206. The query interface 212, and the session manager 208, can maintain and manage context information associated with requests for database operation. In particular implementations, the query interface 212 can maintain and manage context information for requests received through the application manager 210.

When a connection is established between the client 204 and the database server 206 by the session manager 208 or the application manager 210, a client request, such as a query, can be assigned to a thread of the thread pool 224, such as using the query interface 212. In at least one implementation, a thread is a context for executing a processing activity. The thread can be managed by an operating system of the database server 206, or by, or in combination with, another component of the database server. Typically, at any point, the thread pool 224 contains a plurality of threads. In at least some cases, the number of threads in the thread pool 224 can be dynamically adjusted, such in response to a level of activity at the database server 206. Each thread of the thread pool 224, in particular aspects, can be assigned to a plurality of different sessions.

When a query is received, the session manager 208 or the application manager 210 can determine whether an execution plan for the query already exists, such as in a plan cache 236. If a query execution plan exists, the cached execution plan can be retrieved and forwarded to the query language executor 220, such as using the query interface 212. For example, the query can be sent to an execution thread of the thread pool 224 determined by the session manager 208 or the application manager 210. In a particular example, the query plan is implemented as an abstract data type.

If the query is not associated with an existing execution plan, the query can be parsed using a query language parser 228. The query language parser 228 can, for example, check query language statements of the query to make sure they have correct syntax, and confirm that the statements are otherwise valid. For example, the query language parser 228 can check to see if tables and records recited in the query language statements are defined in the database server 206.

The query can also be optimized using a query language optimizer 232. The query language optimizer 232 can manipulate elements of the query language statement to allow the query to be processed more efficiently. For example, the query language optimizer 232 may perform operations such as unnesting queries or determining an optimized execution order for various operations in the query, such as operations within a statement. After optimization, an execution plan can be generated for the query. In at least some cases, the execution plan can be cached, such as in the plan cache 236, which can be retrieved (such as by the session manager 208 or the application manager 210) if the query is received again.

Once a query execution plan has been generated or received, the query language executor 220 can oversee the execution of an execution plan for the query. For example, the query language executor 220 can invoke appropriate subcomponents of the database server 206.

In executing the query, the query language executor 220 can call a query processor 240, which can include one or more query processing engines. The query processing engines can include, for example, an OLAP engine 242, a join engine 244, an attribute engine 246, or a calculation engine 248. The OLAP engine 242 can, for example, apply rules to create an optimized execution plan for an OLAP query. The join engine 244 can be used to implement relational operators, typically for non-OLAP queries, such as join and aggregation operations. In a particular implementation, the attribute engine 246 can implement column data structures and access operations. For example, the attribute engine 246 can implement merge functions and query processing functions, such as scanning columns.

In certain situations, such as if the query involves complex or internally-parallelized operations or sub-operations, the query executor 220 can send operations or sub-operations of the query to a job executor component 254, which can include a thread pool 256. An execution plan for the query can include a plurality of plan operators. Each job execution thread of the job execution thread pool 256, in a particular implementation, can be assigned to an individual plan operator. The job executor component 254 can be used to execute at least a portion of the operators of the query in parallel. In some cases, plan operators can be further divided and parallelized, such as having operations concurrently access different parts of the same table. Using the job executor component 254 can increase the load on one or more processing units of the database server 206, but can improve execution time of the query.

The query processing engines of the query processor 240 can access data stored in the database server 206. Data can be stored in a row-wise format in a row store 262, or in a column-wise format in a column store 264. In at least some cases, data can be transformed between a row-wise format and a column-wise format. A particular operation carried out by the query processor 240 may access or manipulate data in the row store 262, the column store 264, or, at least for certain types of operations (such a join, merge, and subquery), both the row store 262 and the column store 264.

A persistence layer 268 can be in communication with the row store 262 and the column store 264. The persistence layer 268 can be responsible for actions such as committing write transaction, storing redo log entries, rolling back transactions, and periodically writing data to storage to provided persisted data 272.

In executing a request for a database operation, such as a query or a transaction, the database server 206 may need to access information stored at another location, such as another database server. The database server 206 may include a communication manager 280 component to manage such communications. The communication manger 280 can also mediate communications between the database server 206 and the client 204 or the application manager 210, when the application manager is located outside of the database server.

In some cases, the database server 206 can be part of a distributed database system that includes multiple database servers. At least a portion of the database servers may include some or all of the components of the database server 206. The database servers of the database system can, in some cases, store multiple copies of data. For example, a table may be replicated at more than one database server. In addition, or alternatively, information in the database system can be distributed between multiple servers. For example, a first database server may hold a copy of a first table and a second database server can hold a copy of a second table. In yet further implementations, information can be partitioned between database servers. For example, a first database server may hold a first portion of a first table and a second database server may hold a second portion of the first table.

In carrying out requests for database operations, the database server 206 may need to access other database servers, or other information sources, within the database system. The communication manager 280 can be used to mediate such communications. For example, the communication manager 280 can receive and route requests for information from components of the database server 206 (or from another database server) and receive and route replies.

One or more components of the database system 200, including components of the database server 206, can be used to produce a captured workload 290 that includes execution context information 292 and one or more performance measures 294. The captured workload 290 can be replayed, such as after being processed, at another database system.

Example 2 Improved Capture Mechanism and Structure

FIG. 3 provides a diagram of a database environment 300 for implementing a method according to this Example 2 for improving the performance comparison of a first database system 305 with a second database system 310. In some cases, the first database system 305 and the second database system 310 use different versions of the same computer program. In other cases, the first database system 305 and the second database system 310 use the same version of the same computer program, but with different settings. In yet further cases, the first database system 305 and the second database system 310 may use different computer programs for implementing a database system

The first database system 305 is part of a first database environment 315. The first database environment 315 can include one or more clients 320 issuing requests for database operations to one or more application servers 325. The one or more application servers 325 can send the requests for database operations to be carried out by the first database system 305.

In carrying out the requests, the first database system 305 can store information regarding the operations in a persistency layer 335. The persistency layer 335 can include, for example, data stored in a persistent, non-transitory computer-readable storage medium. In addition, the first database system 305 can generate information about the requests, which can be stored, such as in one or more capture files 340. The capture files 340 can include information regarding the request (including the request), data, including metadata, generated during execution of the request, the results of the request, and information about the first database environment 315, the clients 320, or the first database system 305. In at least some cases, the capture files 340 can be stored in a compressed format.

In some cases, each capture file 340, or a particular collection of files includes data associated with, and organized by, a capture unit. The capture unit can be, for example, a session, such as described in Example 1, between a client 320 and the first database system 305 mediated by an application server 325. The session may include one or more requests for database operations, such as one or more statements in a query processing language, such as a query or a transaction. In other cases, the captures file 340, or particular collection of files, represents another processing unit, such as a statement, or a collection of statements over a time period.

The capture files 340 can be processed, such as by the first database system 305, the second database system 310, or another computing system, to produce data, such as replay files 345, suitable for being replayed at a second database environment 350, which includes the second database system 310. The replay files 345 can, for example, decompress information in the capture files 340, or otherwise manipulate the data of the capture files 340 into a form more easily executed at the second database environment 350. In addition to information used for replaying requests for database operations, the capture files 340 can include information that is used to evaluate the performance of the second database system using the captured workload, instead of, or in addition to, being used for replay purposes.

The second database environment 350 can including a replayer component 355. The replayer component 355 may use the replay files 345 to send requests for database operations to the second database system 310 that emulate the requests issued by the clients 320 to the first database system 315.

The system of FIG. 3 can provide a number of advantages. For example, in at least some cases, the capture files 340 can be generated using components of the first database system 305. For example, information in the capture files 340 can include information generated by components of the first database system 305 in carrying out a request for a database operation. The use of existing components, operations, and generated data can reduce the processing load on the first database system 305 in saving a workload, or elements thereof, to be replayed at the second database system 310. In at least some cases, the capture files 340 can include less than all of the information generated during execution of the requests for database operations at the first database system 305, which can also reduce the amount of memory or storage needed to reproduce the workload at the second database system 310. In addition, the conversion of capture files 340 to replay files 345 can be carried out asynchronously and at a different computing system than the first database system 305.

Information included in the capture files 340 can come from one or more sources. In some implementations, capture files 340 can be organized by, or otherwise include data for, capture units, such as database sessions, or another set or subset of requests for database operations. A capture unit, its operations, and data and metadata created during execution of requests for database operations contained in the capture unit (including data returned in response to a query language statement, such as query results), can be associated with a context. In at least some aspects, a context, such as an execution context, is information that describes, or provides details regarding, a particular capture unit, which can be represented by a fact. As described below, the capture unit can be associated with additional facts, such as performance measures.

For example, the session itself may be associated with a session content. The session context can include information such as:

how statements or transactions are committed, such as whether statements are automatically committed after being executed

transaction isolation level, such as read committed or repeatable read

client geographical location

syntax used in the session, such whether strings are null terminated

how deferred writing of large objects is carried out

a connection identifier

a user identifier/user schema

an application identifier

verbosity settings for logging

task execution identifiers

debugger information

As previously mentioned, elements of a session, such as a transaction, can also be associated with a context. A transaction context can include information such as:

snapshot timestamp (such as used for multi-version concurrency control)

statement sequence number

commit ID

updates to a transaction identifier

Similarly, when the statement is a query, such as a query having a query execution plan (as described in Example 1), a plan context can include information such as:

query ID/query string

query plan

compilation time

statement hash

memory statistics associated with the statement or plan

Applications interacting with the database system may be associated with a context, an application context can include information such as:

application name

application user name

application source code identifier

a client identifier

location information

variable mode (such as whether strings are null terminated)

Along with these various contexts, various values, such as facts or performance measures, associated with a workload capture unit, or an element thereof, may be of interest, and stored in the capture files 340. For example, facts or measures may include:

an identifier, such as a timestamp, associated with the capture unit

elapsed time (such as session duration)

processor usage

memory usage

number of executions carried out

number of network calls

number of input/output operations

any waits encountered while the session was active

In some cases, the capture files 340, such as one or more of the contexts and the measure, can include non-deterministic values, such as non-deterministic values associated with a query language statement or its associated operations. Nondeterministic values refer to values that may be different between different computing devices (e.g., different between a database system (or server thereof) where a workload is captured and a database system (or a server thereof) where the workload is replayed. For example, a timestamp function will return a current timestamp value when run on the first database system 305, which may be a different timestamp value than when run at a later time on the second database system 310. Other examples of non-deterministic values include updated database sequence values, generation of random numbers, connection identifiers, and identifiers related to updated transactions.

In particular examples, it can be beneficial to use the same nondeterministic value as used during execution of a request for a database operation at the first database system 305 when the request is carried out at the second database system 410. In implementations where the same value is to be used, the nondeterministic function can be evaluated once (e.g., on the first database system 305) and the resulting value can be provided in the capture files 340 so that when the request (or other workload element) is executed on the second database system 310, the same value will be used (the same value that was used at the workload capture database system).

Although workload capture has been described as occurring with external clients 320, in at least some embodiments, workload capture may also include capture of internal database operations for inclusion in the workload capture files 340. The captured internal operations can be replayed at the second database environment 350. For example, the replay of the captured internal operations at the second database environment 350 may affect the performance of the second database system 310, including the performance of replayed workload elements originating at the clients 320. In other examples, the captured internal operations are not replayed at the replica database system 310, but are used to compare the performance of the first database system 305 with the performance of the second database system 310. For example, the performance comparison can include comparing a number of internal operations generated by the workload at the first database system 305 with a number of internal operations generated by the second database system 310.

In some cases, the internal operations may be triggered by a user. In other cases, the internal operations occur automatically during operation of the database system. For example, with reference to FIG. 2, periodically, the state (such as changed records and redo logs) of the database server 206 can be written as persisted data 272 by the persistence layer 268, such as to create a save point. Save points, in some examples, may be requested by a user. In other examples, save points may occur automatically, such as according to a schedule, when a threshold number of records have been changed, or when a threshold number of request for database operations have been received or executed. Similarly, storage snapshots, file system backups, data backups, and log backup operations can be captured and, optionally, replayed.

Changes to database records, such as records in the column store 264, can, in some examples, be written to temporary copies of the database records. Periodically, the changes reflected in the temporary copies can be merged into the source database records. Making changes to temporary copies can improve the performance of write operations, including concurrent write operations. The temporary copies can, for example, be maintained in an uncompressed state, or state with reduced compression, compared with the primary database records. Merge operations can be captured and included in the capture files 340.

Internal operations can also include housekeeping operations. For example, in some cases, the first database system 305 can have recent or often-used data in-memory, and older, or less commonly used, data can be stored in persistent storage, such as persistent storage 335 or another persistent storage. The capture files 340 can store information regarding the transfer of data between persistent storage and memory.

The capture files 340 may include all or a portion of these contexts, context elements or values, facts, and measures. In addition, the capture files 340 can include additional information, such as information that may be of assistance in replaying the workload at the second database system 310 or in evaluating the performance of the execution of the workload at the first database system 305, such as nondeterministic values.

In at least some implementations, the volume of information that can be collected for a workload can be large. In at least some cases, the volume of information can be reduced by only including a portion of the information, such as a portion of the context information, in the capture files 340. In addition, to assist with organization and compression of the information, in some implementations, the information can be organized in a schema or a dimensional model, such as a star schema. For example, the measures and facts can be used as the central fact table or tables, which reference as points one or more dimensions, each dimension including one or more contexts. In a particular implementation, each point represents a discrete context, and, in addition to any facts or measures, the fact table includes identifiers for corresponding information in the dimensions.

An example of this schema is shown in FIG. 4. FIG. 4 illustrates a capture file 405 that includes a fact table 410 correlated with a plurality of dimensions 415 associated with the fact table 410. Specifically, the fact table 410 is shown as associated with dimensions 415 representing contexts for a session 420, a statement 425, an application 430, a transaction 435, a thread 440, a plan (such as a query execution plan) 445, and other parameters 450. The fact table 410 includes various measures, such as elapsed time and any counters associated with the capture unit associated with the capture file 340 or collection of capture files (such as a session). The fact table 410 also includes dimension IDs (DIMIDs) used to correlate entries in the fact table 410 with each of the dimensional contexts 420, 425, 430, 435, 440, 445, 450. Although a single fact table 410 is shown, in some implementations, multiple fact tables 410 may be used.

The fact table 410 (or tables) and the dimension tables 415 can include records for a plurality of capture units (such as sessions) of the first database system 305. In some cases, the fact table (or tables) 410 and each of the dimension tables 415 can be stored as separate files. That is, there may be one file for the fact table 410 (when a single fact table is used) and one file for each of the dimension tables 415. In a particular example, the capture file 405 can incorporate the files for the fact table 410 and the files for each of the dimension tables 415. In some cases, the capture file 405 can be compressed, such as using a compression algorithm. Similarly, files for one or more of the fact tables 410 and then dimension tables 415 can be compressed. In implementations where a database system from which a workload is captured includes more than one server or node, the capture file 405 can, in some aspects, represent activity occurring at a single node of the first database system 305. In other aspects, the capture file 405 can be associated with multiple nodes of the first database system 305.

The capture file 405 can include additional information. For example, the capture file 405 is shown as including manifest information 455. Manifest information 455 can include, for example, information related to the first database system 305, such as a identifier for the program version used to implement the first database system 305 and information relating to the computing system used to implement the first database system 305 (such as the number and type of processors, amount and organization of memory and physical storage, information related to networked devices implementing the first database system, or configuration information for the first database system 305 or its components). The manifest information 455 can also include a timestamp associated with the capture file 405 (such a time the capture file was generated, a time the workload capture was initiated, or the time workload capture was discontinued). When filters were used to select workload elements to capture, the filter information can also be included in the manifest information 455. When used to store information related to multiple database nodes, the fact table 410 or dimension tables 415 can include information regarding which of the multiple database nodes was involved in executing operations of the workload capture unit, or a subcomponent thereof (such as in executing a statement associated with a particular session). In a particular example, the manifest information 455 can be stored in a file, such as a compressed file, and included within the capture file 405.

The capture file 405 can also include hash information 460. For example, query language statements in the fact table 410 or the dimension tables 415 can be represented by a hash. The hash information 460 can include a hash table correlating hash values to query language statements. By only storing the hash value of the query language statement in the measure file 410 or one or more of the context files 415, the amount of memory needed to store the workload capture can be reduced.

As will be further described in Example 4, hash values can be used to confirm that execution of requests for database operations at the second database system 310 produced the same results as when originally executed at the first database system 305. In some cases, the granularity of hashing can be specified, such as by a user, in such cases, the hash level can be included in the capture file 405, such as in the manifest information 455.

In particular examples, the fact table 410 and the dimension or context tables 415 can be stored as files, and the fact or dimension files grouped together in the context file 405, optionally including a file for the manifest information 455 or a file for the hash information 460. In a particular example, each of the context tables 415 is stored as a separate file within the capture file 405. Each of the capture files can include a dimension identifier (DIMID) field, and one or more value fields. In a specific example, the values can be written in comma separated value format. Similarly, each measure can be stored as a separate file within the capture file 405. Each of the measure files can include a chronological identifier, such as a clock timestamp (such as for the overall database system from which the workload was captured). The chronological identifier can be used, for example, in staging replay of the capture units (such as sessions) represented in the capture file 405. The measure file can also include multiple DIMID fields to associate measures for a particular workload capture unit with its associated dimensions, and one or more numerical fields (such as performance or execution parameters or values). In particular examples, the DIMID or numerical fields can be written in comma separated value format.

In some cases, context files and one or more measure files can be stored for each of a plurality of services in a database system. The set of context files and one or more measure files for each of the services can be included in the capture file 405 (which, in at least some examples, represents a single database server or node, such as when the database system is distributed among multiple servers or nodes). For example, a database system can have separate services that include query language processing components and data stores, that produce information regarding the distribution of information (including database records) and database system components in the database system, that are responsible for handling certain types of client communications, such as web or http-based interactions, that manage various memory stores (such as transferring data between persistent storage and in-memory storage), and that monitor resource (such as CPU or memory) use in the database system (including across multiple database servers, when the database system is a distributed system).

FIG. 5 illustrates components of a database environment 500 that can be used to measure and collect workload capture information to be stored, such as in the capture file 405 of FIG. 4. In at least some cases, the measuring and collecting can be carried out by existing components of a database system. For example, the measuring and collecting can be carried out during normal processing of requests for database operations. In this way, the overhead (such as processor load and delay) associated with the capture process can be reduced.

As shown in FIG. 5, the database environment 500 includes measuring (or execution) components 512, a measurement interface 514, a measurement framework 516, contexts 518, and context management components 520. Each of the measuring components 512 can be responsible for executing various processes associated with a request for a database operation. The measuring components 512 can provide measures or facts associated with a request for a database operation. In some cases, in executing the requests, the measuring components 512 can generate data about the execution of the request, or used in carrying out the request. For example, the measuring components 512 can generate performance information associated with execution of the request.

The measuring components 512 can be registered with the measurement framework 516 using the measurement interface 514. The measurement framework 516 can collect information generated by, or otherwise associated with, the measuring components 512. The measurement framework 516, optionally in conjunction with one or more additional components, can be responsible for generating the capture file 405 of FIG. 4, including the dimension or context tables 415, the fact table or tables 410, the manifest information 455, and the query language hash file 460. In at least some cases, the use of multiple fact or context tables or files allows multiple tables or files to be written in parallel, which can speed the workload capture process.

As discussed above, in at least some implementations, the contexts 518 may include more information than may be needed to replay the requests for database operations in a second database environment, or to compare the performance of the first database system with a second database system. In such cases, a filter layer 524 can be included between the measurement framework 516 and the contexts 518 associated with the measuring components 512. The filter layer 524 can be configured to pass selected information from contexts 518 to the measurement framework 516.

The contexts 518 can be managed, or used, by the context management components 520. In addition, the context management components 520 can be used to provide information to the contexts 518 or to the measuring components 512. In some cases, the context management components 520 can be responsible for information in a context 518 related to dimensional data associated with the context.

Measuring components 512 can include a job executor 522, a query language executor 524, an executor for an application manager 526, a communication service 530, and other components 532 (such as components executing internal database operations, such as merge and savepoint operations). Contexts 518 can include a transaction context 540, a session context 542, a statement context 544, a plan context 546, and other contexts 548 (such as contexts associated with internal database operations). Context management components 520 can include a session manager 550, a query interface 552, a query language processor 554, a transaction manger 556, and others 558 (such as a component managing a context for an internal database operation).

As an example, one context management component 520 can be the session manager component 550, such as the session manager 208 of FIG. 2. The session manager component 550 can coordinate particular sessions, including requests for database operations associated with the sessions. The session manager component 550 can, for example, store information associated with a session in the session context 542. The session context 542 can include values for the parameters identified for the session context table 420 of FIG. 4.

Another of the contexts 518 can be a plan context 546. The plan context 546 can include values for parameters such as described above for the plan context table 445 of FIG. 4. The plan context 546 can, in some cases, be managed by the query language processor 554, such as the query language processor 216 of FIG. 2. The query language processor 554 can also manage a statement context 544. The statement context 544 can include values for parameters such as allowed memory usage, allowed CPU usage, number of allowed parallel threads, priority, user ID, and a sequence identifier.

For a particular capture unit, the measurement framework 516 can aggregate facts and measures, such as performance measures, generated by the measuring units 512 and associate them with the corresponding contexts 518 for the capture unit. The aggregated facts and measures, and the corresponding context information, can be stored in a capture file, such as using the schema of FIG. 4.

FIG. 6 illustrates a database environment 600 depicting a process for storing information from a measurement framework 604 in a capture file 608, such as capture file using the schema of FIG. 4. For each of the capture units, the measurement framework 604 can store a mapping of facts and measurements associated with the capture unit with the corresponding context information. In particular examples, the mapping can be stored in memory, such as in buffers 612 for each of a plurality of contexts and a buffer 614 for measures 618 associated with capture units. FIG. 6 illustrates buffers 612 for contexts 616, including an application context 620, a session context 622, a statement context 624, a transaction context 626, a thread context 628, and, optionally, other contexts 630.

The buffers 612 can be written to the schema of the capture file 608. In some cases, the buffers 612 can be written to separate files 636 for each of the contexts 616, such as files for context data for an application 648, a session 650, a statement 652, a transaction 654, threads 656, and, optionally, other context information 658. The buffer 614 can be written to a measure file 640. The data in the buffers 612, 614 can be compressed prior to being written.

Although a single buffer 614 and a single measure file 640 are shown for the measures 618, in other cases, multiple buffers 614 or files 640 can be used. For example, multiple buffers 614 can be used when the capture file 604 includes multiple measure files 640, or information from multiple buffers 614, such as buffers representing different measures or facts, can be aggregated into one or more measure files 640. The measure file 640 can store information for one or more capture units, including an identifier, one or more facts or measures, and identifiers for context information stored in the context files 636.

In some cases, the buffers 612, 614 may be written to their corresponding files 636, 640 when a workload capture is completed. In other cases, the buffers 612, 614 can be written periodically during workload capture. For example, each of the buffers 612 and the buffer 614 can be assigned a threshold size. If a particular buffer of the buffers 612, or the buffer 614, exceeds the threshold, the buffer can be written to its corresponding file 636, 640 and emptied. In other cases, the buffers 612, 614 can be written periodically in another manner, such as at particular time intervals or after a particular number of capture units have been added to the buffers. When the workload capture process has been completed, the files 636, 640 can be combined, such into the capture file 405 of FIG. 4. In particular examples, the files 636, 640 can be compressed as they are combined.

The database environment 600 may be implemented in a different manner. In a particular implementation, rather than a single buffer for each of the contexts 616 and the buffer 614 for the measures 618, the database environment can include multiple buffers. For example, two (or more) buffers can be included for each of the contexts 616 and for the measures 618. Data can be written to the first buffers until a buffer is filled, at which time it can be written to a file as described above. During the writing process, information can be stored in the second buffer, which is then written when filled, and new data stored in the first, now empty, buffer. Also, rather than having separate buffers for each of the contexts 616 and the measures 618, the contexts and/or measures may be stored in a common buffer. When filled, the buffer can be written to respective context and measure files 636, 640. The environment 600 can include one or more additional common buffers to be used when a first common buffer is being written.

FIG. 7 is a diagram of a process 700 for comparing performance statistics of a workload executed at a first database environment with performance statistics of the workload executed at a second database environment. In step 705, a workload, such as one or more requests for database operations (which may be associated with one or more database sessions) is captured at a source system, such as described in conjunction with FIGS. 2-5. The capture process 705 generates a capture file 710, such as a capture file having the structure of the capture file 405 of FIG. 4.

The capture file 710 is processed in process 715 to produce captured requests for database operations and associated data in a replayable format 720. The processing in step 715 can include extracting or decompressing individual context and measure files from the workload capture file 710. If the individual context and measure files were compressed, they can also be extracted or decompressed.

During step 715, individual capture units, such as sessions, and/or subcomponents thereof (e.g., statements) can be reconstructed from the context and measure files. For a particular measure, relevant context information can be retrieved based on dimension or context identifiers stored with the measure. For example, statements in a session can be associated with corresponding context information, such as a query language statement and context information (or triggers to generate the context information) that would have been received by the database system from which the workload was captured.

In some cases, the capture units, and operations within the capture units, can be ordered during the processing step 715. For example, the capture units, or operations, can be ordered chronologically use a time associated with the capture units or operations (e.g. a system timestamp, commit timestamp, or other identifier). In other cases, the capture units are reconstructed during processing step 715, but are not ordered. Ordering can occur, for example, during replay of the replayable format 720.

In particular examples, the replayable format 720 can be a replay file, such as a file stored on a hard disc or other persistent storage medium or non-volatile memory. In other examples, the replayable format 720 can be stored in a different manner, such as in volatile memory. While in some cases the replayable format 720 may be a single store or file, in other cases information in the repayable format 720 can be included in multiple stores or files.

The replayable format 720, including a replay file, can include information regarding the workload capture process and workload capture system, such as a database software version associated with the source data system, information regarding the configuration (including processors and available memory) of the source database system, and start and end times for the workload capture. The replayable format 720 can also include information regarding any filters applied to workload elements being captured, such as workload elements associated with particular applications, users, clients, statement types, or elements exceeding a threshold duration. Statistics regarding the capture process, such as the number of capture units (e.g., sessions), statements, and/or committed transactions recorded in the replayable format 720 can also be stored in the replayable format 720. The replayable format 720 can further include information regarding the processing step 715, such as a software version associated with a processing component generating the replayable format, information about a computing system used to carrying out the processing step, and start and end times for the processing.

The replayable format 720 can include additional information. For example, information can be included regarding database users, such as information that would have been transmitted to the source database system when the workload was generated. Information regarding query language elements can be included in the replayable format 720, such as codes for DDL (data definition language), DML (data manipulation language, e.g., SELECT, UPDATE), and DCL (data control language) operations. The replayable format 720 can also include information regarding query language statement strings, such relating an identifier (such as a hash value) to a particular query language statement string.

Other types of information that can be included in the replayable format 720 include information regarding the number of batch updates at the source database system during workload capture, values of nondeterministic functions, and information regarding nested statement in workload capture units, or elements thereof (such as in CALL statements). Hash value results, such as those used to verify consistent execution between the source database system and the second database system can be included in the replayable format 720.

The replayable format 720 can also include information, including in individual files, related to individual capture units, such as database sessions. Information for the capture units can include connection identifiers, information regarding the capture unit, or suboperations thereof, such as a start time for a request, a request type, and context information associated with the request. Parameters, such as measures, associated with the capture unit can be included in the replayable format 720. The replayable format 720 can also include identifier information for the capture unit, such as a session ID, a user name, a particular schema associated with the user, etc.

In optional process 725, the captured workload in the replayable format 720 can be customized to produce a customized captured workload in a replayable format 730. For example, process 725 can include selecting particular users, clients, applications, sessions, etc. associated with the first database environment to be replayed at the second database environment. A user may also select particular segments of a captured workload to be replayed, rather than replaying an entire workload, or can merge multiple workloads (or segments thereof) to be replayed, such as simultaneously or sequentially. In some aspects, a user can also select the speed at which the workload should be replayed. That is, in some cases, requests for database operations can be replayed with the same time interval experienced at the source system. In other cases, the user can select the workload to be replayed in another manner, such as increasing or decreasing the replay speed versus the speed database operations were carried out at the source database system.

In process 735, the captured workload in the replayable format 720, or the customized workload in replayable format 730, is replayed at the second database environment. The replay 735 can include reading information associated with individual capture units, or operations thereof, from the replayable format 720. In some cases, the information can be read sequentially from the replayable format 720. In other cases, a replayer component can select capture units, or operations thereof, to be replayed in a particular order. For example, the replayer may select capture units, or operations, in the replayable format 720 to be replayed based on order in which the capture units or operations were received by the source database system, such as a chronological order. The chronological order can be determined, for example, using an identifier (e.g., a system timestamp, a commit timestamp, or other identifier).

In at least come cases, the replayer cause multiple capture units, or operations, to be sent to, or replayed at, the second database system. In particular implementations, workload elements received by the second database system, to be replayed, can be received in a format analogous to the format in which they were received by the source database system. That is, the replay information can resemble requests for database operations produced by an external database client (or by internal database operations of the source database system). The replay 735 produces an output 740, such as workload replay report data, that includes performance measures associated with the execution of the workload at the second database environment.

A workload report process 745 can be carried out, such as comparing workload replay report data with workload capture report data 750, such as data including performance measures associated with the execution of the workload at the first database environment, such as performance measures associated with the capture file 710, the workload in the replayable format 720, or the workload in the customized workload replayable format 730. In at least some implementations, one or both of the workload capture report data 750 and the workload replay report data 740 can be structured as, or include information in, the capture file 405 of FIG. 4.

The workload report process 745 can produce a workload report 755, such as a report comparing execution of the workload at the first database environment with execution at the second database environment. For example, the workload report can include information regarding processor usage, wait times (such as average execution time), query language statement throughput (such as the number of executed statements), number of connections with other database servers or other components, query performance, comparisons of query results or query plans between two database systems, candidate and execution locations of statements, and information regarding the consistency of tables between the two database environments.

The workload report 755 can include information comparing and summarizing the performance of the entire workload replay, or individual elements of the workload replay. For example, the report 755 can provide information comparing the execution of particular capture units, or particular operations within a capture unit. In a particular example, the report 755 is stored as database records or is derived from database records, such as in an OLAP cube. FIG. 8 illustrates an OLAP cube 800, and dimensions 810 and measures 820 that can be included in the cube. In some cases, the OLAP cube 800 can be a hypercube, having more than three dimensions 810.

FIG. 9 presents a method 900 of generating one or more workload capture files for a database system. In step 910, the database system receives a plurality of requests for database operations. The plurality of requests are executed in step 915. For the plurality of requests, one or more components of the database system generates execution context information for a plurality of execution contexts in step 920. In step 925, for the plurality of requests for database operations, one or more components of the database system generates a plurality of performance measures associated with the requests. The execution context data and the performance measures are stored in step 930. In at least some implementations, the performance measures and the execution context data can be stored in an analytical schema, such as a star schema. For example, the performance measures can be stored as one or more fact tables associated with one or more dimension tables constructed from the execution context data.

In particular examples, the request for a database operation is a first request for a database operation from a first database client and the method 900 includes receiving a second request for a database operation. Execution context data and performance measures are generated for the second request. The execution data and performance measures for the second request for a database operation are stored with the execution context data and the performance measure for the first database operation, such as in the schema.

FIG. 10 illustrates a method 1000 for generating a workload replay file, such as from a workload capture file or other workload capture data. Workload capture data, such as a workload capture file, is received in step 1010. The workload capture data includes stores, such as files, for each of a plurality of execution context and at least one store, such as a file, for performance measures. In step 1015, the plurality of execution context stores and the at least one performance measure store are extracted to provide execution context data and performance measure data. For each of a plurality of workload capture units, execution context data and performance measure data associated with the respective workload capture unit are collected in step 1020. For each of the workload capture units, in step 1025, the collected execution context data and performance measure data are stored in a format replayable by a second database system.

A method 1100 for comparing workload statistics associated with a first database system with workload statistics associated with a second database system in illustrated in FIG. 11. In step 1110, replay data associated with a workload captured at a first database system is received. The workload replay data includes one or more requests for database operations and first performance data associated with the one or more requests. The one or more requests for database operations are executed at the second database system in step 1115. In step 1120, second performance data associated with the execution of the one or more requests for database operations at the second database system are generated. The first and second performance data are compared in step 1125.

Example 3 Replay Initiation with Dynamic System

In some implementations of the present, a second database system can be assumed to not include elements of the workload (such as sessions, or query language statements associated therewith) captured at a first database system. However, in other cases, a workload captured at the first database system can include requests for database operations, or components thereof, that have already been carried out at the second database system. Replay of workload elements at the second database system that have already been carried out by the second database system can lead to inaccurate performance measurements, can potentially cause a database system of the second database system to have different contents than it should—which can potentially affect the outcome of subsequent replay operations.

Each transaction, such as a transaction that writes, updates, or deletes a record of a database table, can be associated with a commit timestamp, such as a commit log sequence number. The state of a database is also typically associated with a snapshot that includes a commit timestamp. When a transaction is to be replayed, its commit timestamp can be compared with the commit timestamp of the second database system. If the transaction commit timestamp is lower than or equal to the snapshot commit timestamp of the second database system, the transaction can be selected to not be replayed. If the transaction commit timestamp is larger than the snapshot commit timestamp of the second database system, the transaction can be replayed, as it is not yet represented in the second database system.

FIG. 12 depicts a database environment 1200 having database operations T1 though T6. A workload is captured at the first database system at time T₀. An image of the first database system, against which transactions will be replayed at the second database system, is acquired at time T₁. During replay, the commit timestamp of transactions T1-T6 are compared with the snapshot commit timestamp associated with T₁. Transactions T1-T3 started before T₀ and were completed before T₁. Accordingly, during workload replay, transactions T1-T3 can be skipped. Transaction T5 started after T₁ and therefore will be replayed, as it is not represented in the database image captured at T₁.

Transactions T4 and T6 started before T₁, but were not completed (committed) when T₁ was acquired. Similar to recovery from a backup, when the database image acquired T₁ is activated at the second database system, open, uncommitted transaction can be rolled back. Thus, during replay, T4 and T6 will not be represented in the second database system, and can be replayed.

FIG. 13 illustrates a method 1300 for determining whether a captured query language transaction captured in a workload, such as in a workload capture file or a workload replay file, should be replayed. In step 1310, a transaction is read from a captured workload including execution context data and execution measures associated with the execution of a plurality of requests for database operations at a first database system. In step 1320, a state identifier, such as a timestamp (e.g., a commit timestamp), is determined for a copy of the first database system. In step 1330, a state identifier, such as a timestamp (e.g., a commit timestamp) of the transaction is determined. The state identifier for the transaction, in step 1340, is compared with the state identifier of the copy of the first database system. The transaction is replayed at the second database system in step 1350 if the state identifier of the transaction is larger than the state identifier of the copy of the first database system.

Example 4 Query Result Validation

In carrying out an example of this disclosure, such as Example 2, it can be beneficial to confirm that a captured workload replayed at a second database system produced the same results as when it was originally executed on a first database system. Thus, according to this Example 4, methods and systems of the present disclosure can provide for confirming that the replay outcome matches the original execution outcome.

In a particular implementation, a hash value can be generated for one or more elements of a captured workload. A hash value can be generated when the corresponding element (or elements) is replayed at the second database system. If the hash values are identical, it may indicate that the element was executed identically at the first and second database systems.

FIG. 14 illustrates a database environment 1400 providing for hashing of captured workload elements. Requests for database operations are carried out by components of a database server 1410. For example, results may be generated by one or more execution components 1414 of the database server 1410, such as the SQL processor 216 of FIG. 2, or one of its subcomponents. In addition to being passed to a session manager component 1418 (and in turned returned to one or more applications 1422), the results can be passed through one or more hashing functions 1426. The hash value can be associated with the result (such as an identifier for the result, or the identifier for a request for a database operation (or the execution thereof). The identifier and hash can be stored, or otherwise associated, with a captured workload, such as with a capture file 1430. In at least some cases, the capture file 1430 can be structured in an analogous manner to the capture file 405 of FIG. 4.

In particular implementations, hashing can be carried out at different levels of granularity. Optionally, multiple levels of hash granularity can be obtained for a request for a database operations, or components thereof. For example, when the workload capture unit is a session, hashing can be carried out for information related to the overall results, such as the results (such as for an entire set of identifiers and/or values returned for the session) or meta information related to the result records. Such meta information can include, for example, the type of result, the length of the results, or numeric contents of the result.

At a finer level of granularity, individual result records can be hashed (such as identifiers and/or results for all columns in a particular result), including meta information related to such records (such as type, length, numeric constants, and length of string data). At a yet finer level of granularity, subcomponents, such as individual table columns in the results, can be analyzed using a hash function. Increasing the granularity of hashing can improve accuracy in verifying that the same results were achieved by the first and second database systems. However, finer levels of granularity typically result in higher overhead for the verification process, both in storing a larger number of hash values, and computational overhead in generating the hash values at the first and second database systems, and then comparing them.

In at least some cases, the system 1400 can provide for determining hash values at multiple levels of granularity. In some cases, multiple levels of granularity may be simultaneously generated by the first database system. In other cases, a user may specify a level of granularity to be used, referred to as a hashing level. In order for the hash values to be compared, the second database system typically should apply the same hash level as the first database system. The hash level, in some implementations, can be included in workload capture information sent to the second database system. For example, the hash level can be included in the capture 405 file of FIG. 4. In a more particular example, the hash level can be included in the mangiest information 1435, which can be at least similar to the manifest information 455 of FIG. 4. The capture file 1430 can also include hash information 1440, such as hash values associated with the hashed data of the captured workload (such as in one or more measure files or context files).

FIG. 15 illustrates hash generation and comparison at a second database environment 1500. The second database environment 1500 includes a replayer 1505 and a second database system 1510. The replayer 1505 include a query language interface 1515 for sending query language statements to the second database system 1510.

The second database system 1510 can include a session manager component 1520, one or more execution components 1525 (such as described in conjunction with the database environment 200 of FIG. 2), and a hash component 1530. The session manager component 1520 can receive requests for database operations from the replayer 1505, such as requests generated through processing of a captured workload, such as a capture file 1535 (e.g., capture file 405 of FIG. 4). The capture file 1535 can include manifest information 1540, which can specify a hashing level or a hashing function.

The session manager component 1520 can forward the requests for database operations to the execution components 1525, or other appropriate components of the second database system 1510. The execution components 1525 (or other components) can generate execution results that are passed to the hashing component 1530. When multiple hash levels are available, the hashing component 1530 can determine a hashing level used at a first database system used to capture the workload to be replayed. For example, the hashing component 1530 can read the hashing level from the manifest information 1540. The hashed values generated by the hashing component 1530, and the results, or identifiers associated with the results, can be returned to the replayer 1505.

The replayer 1505 can include a comparator component 1550. The comparator 1550 can receive hash information 1545 from the capture file 1535. The comparator component 1550 can, for a particular result unit represented by the hash values from the first and second database systems, compare the hash values to determine whether they are the same and generate verification results 1555. In some cases, verification can be skipped for at least certain types of requests for database operations, such as those involving non-deterministic functions or values (particularly when the value at the source system is not included in the workload capture file 1535). The verification, in some examples, is carried out synchronously with the replay process. In other examples, the verification can be carried out asynchronously. In some cases, hash generation or verification for large objects can also be skipped. When a query language statement includes a query that can generate multiple result sets, verification can account for the relationship of the result sets to ensure that the replay hash values are generated appropriately.

The result of the comparison, whether the hash values were or were not the same for the first and second database systems, can be stored, such as in a result table 1560. In a particular example, the result table 1560 can be stored on a control system 1565, such as a computing system in communication with the first database environment, the replayer 1505, and the second database system 1510.

Typically, results are sent in units having a particular size, such as specified by an amount of memory or number of lines. If hash values are returned to the replayer 505 along with execution results, more request fetches may be required to send the same amount of results compared with the number of fetch operations carried out at the first database system. In some cases, if the same number of fetch requests are required, the size of the fetch requests can be increased. However, this may result in higher memory usage at the second database system 1510 compared with the first database system, which may skew a performance comparison between the two systems. In other cases, the fetch request size at the second database system 1510 can be maintained at the same size as the source database system. However, the performance metrics of the second database system 1510 can be adjusted such that the number of fetch requests at the second database system can be adjusted so that only the number of fetch requests required to transmit result data are used in determining performance metrics. For example, the number of fetch requests required for a particular operation can be determined as the size of the execution results divided by the size of a fetch request. In other cases, the number of fetch requests needed to transmit execution results, versus the number needed to transmit execution results and hash values, can be simulated in another manner.

In further implementations, the system 1500 can be arranged differently, or the generation of hash values by the second database system 1510, the comparison of hash values, or the storing or other use of the comparison can be carried out in a different manner. For example, in some cases, the comparator component 1540 can be located elsewhere, such as within the control system 1550. Similarly, rather than being stored in the control system 1565, the result table 1560 can be stored elsewhere, such as the replayer 1505.

FIG. 16A illustrates a method 1600 for determining hash values for a captured workload according to this Example 4. In step 1610, execution data associated with the execution of a request for a database operation, such as query language statement or component thereof, is generated. The data is hashed in step 1620 to produce a hash value. In step 1630 the hash value is stored in a capture file with execution context data and performance data associated with the request for a database operation.

FIG. 16B illustrates a method 1650 for verifying the execution of a replayed request for a database operation. In step 1660, a request for a database operation of workload capture data is executed at a second database system to generate execution data. In step 1670, a hash value is calculated for the execution data. The calculated hash value is compared, in step 1680, with a stored hash value associated with the execution of the request for a database operation at a first database system.

Example 5 Replay Capture with Distributed Nodes

In some cases, a database environment may include multiple database servers, which may function as nodes in a database system. A node can serve as a source or master node, and other nodes may serve as replica or slave nodes. In a particular example, a particular node may function as both a source node and a replica node, such as depending on the operations used by, or records accessed with, a particular request for a database operation.

FIG. 17 illustrates a database environment 1700 having a client 1710 and database nodes 1715 (Node 1), 1720 (Node 2), 1725 (Node 3). Database Node 1 1715 holds a copy of Table 1, database Node 2 1720 holds copies of Tables 2 and 3, and database Node 3 1725 holds a copy of Table 2. In some cases, a database system including the nodes 1715, 1720, 1725 can determine which of the nodes should service a particular request for a database operation, such as a query language statement, from the client 1710. The determining can include determining which of the nodes 1715, 1720, 1725 holds records relevant to the request, or is otherwise appropriate to service the request. In particular examples, more than one of the nodes 1715, 1720, 1725 may be capable of processing a particular request. The database system can determine which of the nodes 1715, 1720, 1725 should receive and process such a request. Once a determination is made as to which node 1715, 1720, 1725 should receive a request, the routing information can be sent to, and stored by, the client 1710. In such a way, on future requests including the statements S1-S3, the client 1710 can route the statement directly to the appropriate node 1715, 1720, 1725.

Client 1710 is illustrated as having a plurality of requests for database operations, including a statement S1 1730 having a SELECT from Table 1, a statement S2 including an UPDATE at Table 2, and a statement S3 having a SELECT from Table 3. The location where the statements S1-S3 should be carried out was previously determined by the database system (such as by one of the nodes 1715, 1720, 1725, including a node operating as source node or a node operating as a replica node) and was cached in corresponding caches 1735, 1740, 1745 for each of the respective statements. Specifically, statement S1 has a cached routing information 1745 indicating that S1 should sent to Node 1 1715. Statement S2 and statement S3 have cached routing information 1750, 1755 indicating that the statements should be sent to Node 3 and Node 2, 1725, 1720, respectively.

In some aspects, routing information stored in the caches 1735, 1740, 1745 can be changed. For example, after executing a statement, the database system operating nodes 1715, 1720, 1725 may determine that a different node should process the request than the previously cached node. The change may be made because it was determined that another node may service the request more quickly, for load balancing purposes, or because of changes to the location of records accessed by the statement. In FIG. 17, while statement S2 presently has cached routing information 1750 indicating that Node 3 1725 should process the statement, the statement S2 could also be processed by Node 2 1720. Thus, in appropriate, the database system could replace the value in cache 1750 with Node 3 1725.

According to this Example 5, methods, systems, and computer-implemented instructions for implementing a workload capture and replay system can include capturing routing information indicating which of a plurality of nodes of a source database system is responsible for processing a particular request for a database operation, or portion thereof, such as a statement. FIG. 18 illustrates a database environment 1800 having a first database server 1810 of a first, such as a source, database system. The first database server 1810 may be, for example, a node in the first multi-node database system, such as a replica node. The first database server can be in communication with one or more applications 1815 which issue requests for database operations to the first database server. During workload capture at the first database system, such as described in conjunction with Example 2, such as summarized in FIG. 3, the first database server 1810 can collect and store, such as using the context framework 516 and associated components of FIG. 5, routing information associated with a request for a database operation, or a portion thereof, such as a statement. The routing information, in a particular example, is a volume identifier or other identifier specifying a particular database node responsible for processing the request at the first database system. The node identifier can be stored in a capture file 1820, which may be structured in a similar manner as the capture file 405 of FIG. 4.

The node identifier stored in the capture file 1820 can be read by a replayer 1825 during workload replay at a second database system 1830 that can include a master node 1835 and a slave node 1840. Slave node 1840 can include records corresponding to the first node 1810. During replay, the replayer 1825 can prepare the request, such as a query language statement, for execution, including the identifier of the node to be accessed for the request. In return, the master node 1835 can return to the replayer 1825 a routing location for the slave node 1840. The replayer 1825, such as using a query language interface 1845, can send the request to be executed at the slave node 1840. In at least some cases, the routing location for the statement can be cached at the replayer 1825 to be used if the request occurs again during workload replay.

FIG. 19 illustrates a method 1900 of replaying a captured workload of a first, distributed database system at a second, distributed database system. In step 1910, a workload capture file is received that includes a request for a database operation executed at least in part at a slave node of the first database system. The request is associated with an identifier of the slave node of the first database system. In step 1915, the request for a database operation is sent to a master node of the second database system with the identifier of the slave node of the first database system. The master node, in step 1920, returns an identifier for a slave node of the second database system to execute the request. In step 1925, the request for a database operation is forwarded to the slave node of the second database system for execution.

Example 6 Selective Workload Capture

In at least some aspects, workload capture according to an example of the present disclosure can reduce processing loads and storage requirements for generating and storing a captured workload. However, particularly for heavily-used database systems, workload capture can still require significant processing or storage. In addition, capture of less than all workload events may be sufficient to, for example, evaluate the performance of a second database system compared with the first database system.

According to this Example 6, a mechanism can be provide to allow a user, such as a database administrator, to select workload elements to be captured when a new workload capture process is initiated. FIG. 20 is an example UI screen 2000 illustrating workload capture options that may be set by a user in a particular implementation of this Example 6. In other implementations, a user may select more, fewer, or different options than presented in FIG. 20. In addition, the UI interface screen 2000 may be structured differently, or the user input specifying capture details can be specified other than through a UI screen. Although shown in conjunction with workload initiation, elements of the screen 2000 allowing workload capture to be defined (including filter criteria) can be included in an analogous UI screen to edit the properties of a previously created workload capture.

Screen 2000 includes a Capture Name field 2010, where a user can enter a name to identify the particular workload capture process being defined. Screen 2000 includes a schedule field 2008, where a user can select whether the workload capture process should be carried out according to a schedule, such as being set to occur at a defined time, or to recur at particular times or intervals. Through field 2012, a user may select whether to collect an execution plan associated with a query language statement, such as a query plan. Similarly, the user may be provided with an option to collect performance details associated with the workload/workload capture units or elements through field 2016. Collecting execution plans and performance details can enable more detailed comparisons to be made between database systems executing the workload, but can increase processor and memory use.

A filter portion 2020 of the screen 2000 can provide a user with filter elements that may be selected for the workload. In at least some aspects, the use of a filter is optional. In some cases, a user may select multiple filter criteria to be applied to workload capture. In other cases, a user may be limited to selecting a particular filter criterion or particular combinations of filter criteria.

The filter portion includes fields allowing a user to select only workloads originating from selected applications 2022, particular database users 2024, clients 2026, and application user names 2028. Using field 2030, a user can select one or more types of statements to be captured. For example, statements can include data manipulation statements (e.g., DELETE, INSERT, REPLACE, SELECT, UPDATE), data definition language statements (e.g. ALTER TABLE, CREATE INDEX, CREATE SCHEMA, CREATE SEQUENCE, CREATE STATISTICS, CREATE TABLE, CREATE VIEW), procedure statements (e.g. CALL, CREATE FUNCTION, CREATE PROCEDURE, CREATE TYPE), transaction statements (e.g. COMMIT, LOCK TABLE, ROLLBACK, SET TRANSACTION), session statements (e.g. CONNECT, SET HISTORY SESSION, SET SCHEMA, SET [SESSION], UNSET [SESSION]), or system statements (e.g. ALTER SYSTEM CONFIGURATION, ALTER SYSTEM SESSION SET, ALTER SYSTEM SAVE PERFTRACE, ALTER SYSTEM SAVEPOINT).

With a trace level field 2034, a user can select that only particular statements associated with a particular tracing level be included in the workload. For example, statements may be associated with a high, medium, or low tracing level. As tracing level moves from low to high, an increasing amount of information can be captured for the statements, such as access level logging for low tracing levels, packet logging at a medium trace level, and entire statement contents being traced at a high level of tracing. In particular implementations, a user may select to change the tracing level normally associated with a particular type of statement such that the statements are captured by the workload capture filter.

In field 2038, a user can select that only statements meeting or exceeding a threshold duration are captured. For example, if there are a significant number of requests for database operations, such as requests which include queries, capturing only requests (or components thereof) exceeding a threshold can reduce any performance impact of the workload capture process.

In field 2042, a user can select a statement hash level to be used in the workload capture, such as described above in Example 5. The hash level can be used to determine the granularity of result verification during the replay process. While a higher granularity can improve result verification, it can result in higher resource use at the workload capture database system and the replay database system.

FIG. 21 illustrates a method 2100 for specifying workload capture filter criteria and capturing a database workload meeting the filter criteria. User input is received in step 2110 specifying workload capture filter criteria, such as requests for database operations originating with particular users or applications, or request meeting other selected criteria. In step 2115, the database system receives a request for a database operation. A component of the database system generates execution context data associated with the request in step 2120. In step 2125, a component of the database system generates performance measures associated with the request.

It is determined in step 2130 whether the request meets the filter criteria. If the filter criteria are met, the associated execution context data and the performance measures are stored in step 2135. In at least some implementations, the performance measures and the execution context data can be stored in an analytical schema, such as a star schema. For example, the performance measures can be stored as one or more fact tables associated with one or more dimensions represented by the execution context data.

Example 7 Incremental Preprocessing of Replay Operations

FIG. 6 of Example 2 provides a general depiction of how workload capture files generated from a workload at a first database system may be converted to replay files suitable for being replayed at a second database system. In some cases, the conversion can be carried out in a single process. This Example 7 provides a method for carrying out the conversion process incrementally, which can increase conversion speed and efficiency.

With reference to FIG. 22, in at least some implementations, the conversion of one or more capture files into one or more replay files can be represented by the process 2200. The process 2200 include an extraction process 2210, a loading process 2212, a queuing process 2214, and a writing process 2216. In other implementations, one or more of processes 2210, 2212, 2214, and 2216 may be combined, or may be further divided into additional processes.

In extraction process 2210, a capture file 2220 can be read by one or more executor threads 2222. In particular examples, the thread or threads 2222 read the capture file 2220 sequentially. The executor threads 2222 can decompress a portion of the capture file 2220, such as into files for various database services (such as described in Example 2), including a service providing query language processing and a service providing location information for data and database system components). The executor threads 2222 can write the contents of the capture file 2220 as one or more files 2226, such as compressed files, associated with a service. Each service may include a plurality of files 2226. For example, a particular service may be associated with multiple context (or dimensions) files and one or more measure (or fact) files.

The files 2226 can be placed in a queue 2230 for the loading process 2212. In the loading process 2212, each service may be associated with a loader thread group 2232. Each loader thread group 2232 can read appropriate files 2226 from the queue 2230 to determine which elements of the files 2226 are associated with a particular capture unit, such as a session. Elements of files 2226 from different loading processes 2226 (such as from different services) can be combined based on their capture unit (such as a session). In some cases, the capture unit can be represented in an encoded format, such as a hash value. In particular aspects, a particular request for a database operations can include nested statements or operations. These statements or operations can, in some cases, be executed in parallel at multiple nodes of the database system. In such cases, the statements may be included in multiple workload capture files 2220, but can be combined during processing of the capture file or files 2220 during conversion to replay data, including the process shown in FIG. 22.

As the files are loaded by the loader thread groups 2232, context and measure information related to individual captures units, such as sessions (and their component operations) can be retrieved or collected by the loader thread groups and added to a queue 2238 in the queuing process 2214. The queue 2238 holds the information until it is ready to be written to individual stores, such as files, for the session. For example, multiple stores may be generated for context information, organized in a format useable by a replayer component to replay the workload at a second database system.

In some implementations, the queue 2238 can be a table partitioned by session. Information in the queue 2238, in particular examples, can be structured in a similar manner as context and measure information was associated with the session at the capture database system. For example, the queue 2238 can employ the star schema of FIG. 4.

The loader thread groups 2232 can maintain information about the minimum timestamp (e.g., a system timestamp, commit timestamp, or other identifier) of information to be read by the loader thread groups for their particular service. The loader thread groups 2232 can update a global timestamp 2236, which represents the minimum timestamp among all of the services being processed by the loader thread groups. The global timestamp 2236 can be compared with a timestamp for the session (or other capture unit). When the global timestamp 2236 is greater than (or, in some cases, greater than or equal to) the timestamp for a session, the session can be written to a store.

As a session (or other capture unit) is completed, and writer threads 2240 of the writing process 2216 are available, each session can be written (such as to a file or in memory) as request data 2244 and associated parameter data 2246. In some cases, request data 2244 can include context and measure information used to replay the session (including requests for database operations within the session). Request data 2244 can include performance data usable to compare execution of the requests with the execution of the requests at the workload capture database system. Parameter data 2246 can include parameters used in executing the requests, such as a statement string and parameter values used in executing query language operations.

The writer threads 2240 can combine context and measure data from the queue 2238 as appropriate for the sessions, and requests for database operations thereof. Within a session, requests for database operations, and components thereof (such as statements, and operations associated with statements), can be ordered by the writer threads 2240 during the writing process 2216, such as chronologically (such as by using timestamps, for example, a system timestamp, a commit timestamp, or another identifier), so that the requests will reproduce the workload of a first database system where the requests were captured when the requests are carried out by a second database system. For example, statements (or operations thereof) can be ordered within a session by a system clock timestamp or a global commit timestamp.

In some cases, when a session is written to the files 2244 and 2246, the data can be removed from the queue 2238, which can limit the amount of memory or storage needed for the queue. In other cases, the queue 2238 can be persisted, such as for use in comparing the performance of the database system where the workload is replayed to the performance of the workload capture database system.

The request data 2244 can include information to be replayed, such as by the replayer 355 of FIG. 3. Parameter data 2246 can include performance measures associated with execution of the session (or other unit) at the first database system, to be compared with performance measures generated during execution of the workload at the second database system (e.g., step 745 of FIG. 7). In at least some cases, the request data 2244 and parameter data 2246 can be replayed and used for comparison purposes multiple times. For example, the request data 2244 can be replayed at the second database system using different performance or operational settings. The performance at the various settings of the second database system can be compared with one another, and with the parameter data 2246.

FIG. 23 illustrates a method 2300 for incrementally processing a workload capture file to produce workload replay data. In step 2310, a workload capture file is received. The workload capture file includes a plurality of context files and at least one measure file. The context files and at least one measure file are extracted from the workload capture file in step 2315. In step 2320, context data and measure data of the context files and at least one measure file are read. The context data and measure data are associated with a workload capture unit, such as a session, in step 2325. In step 2330, the context data and measure data for the workload capture unit are used to generate request data and parameter data for the workload capture unit, such as a request data file and a parameter data file.

Example 8 Computing Systems

FIG. 24 depicts a generalized example of a suitable computing system 2400 in which the described innovations may be implemented. The computing system 2400 is not intended to suggest any limitation as to scope of use or functionality of the present disclosure, as the innovations may be implemented in diverse general-purpose or special-purpose computing systems.

With reference to FIG. 24, the computing system 2400 includes one or more processing units 2410, 2415 and memory 2420, 2425. In FIG. 24, this basic configuration 2430 is included within a dashed line. The processing units 2410, 2415 execute computer-executable instructions, such as for implementing a database environment, and associated methods, described in Examples 1 and 2. A processing unit can be a general-purpose central processing unit (CPU), processor in an application-specific integrated circuit (ASIC), or any other type of processor. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power. For example, FIG. 24 shows a central processing unit 2410 as well as a graphics processing unit or co-processing unit 2415. The tangible memory 2420, 2425 may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two, accessible by the processing unit(s) 2410, 2415. The memory 2420, 2425 stores software 2480 implementing one or more innovations described herein, in the form of computer-executable instructions suitable for execution by the processing unit(s) 2410, 2415. The memory 2420, 2425, may also store database data, such as data in the row store 162 or the column store 164 of FIG. 1.

A computing system 2400 may have additional features. For example, the computing system 2400 includes storage 2440 (such as for storing persisted data 172 of FIG. 1), one or more input devices 2450, one or more output devices 2460, and one or more communication connections 2470. An interconnection mechanism (not shown) such as a bus, controller, or network interconnects the components of the computing system 2400. Typically, operating system software (not shown) provides an operating environment for other software executing in the computing system 2400, and coordinates activities of the components of the computing system 2400. In some cases, the operating system can manage, or assist in managing, query language execution threads or job execution threads.

The tangible storage 2440 may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other medium which can be used to store information in a non-transitory way and which can be accessed within the computing system 2400. The storage 2440 stores instructions for the software 2420 implementing one or more innovations described herein.

The input device(s) 2450 may be a touch input device such as a keyboard, mouse, pen, or trackball, a voice input device, a scanning device, or another device that provides input to the computing system 2400. The output device(s) 2460 may be a display, printer, speaker, CD-writer, or another device that provides output from the computing system 2400.

The communication connection(s) 2470 enable communication over a communication medium to another computing entity, such as another database server. The communication medium conveys information such as computer-executable instructions, audio or video input or output, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media can use an electrical, optical, RF, or other carrier.

The innovations can be described in the general context of computer-executable instructions, such as those included in program modules, being executed in a computing system on a target real or virtual processor. Generally, program modules or components include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Computer-executable instructions for program modules may be executed within a local or distributed computing system.

The terms “system” and “device” are used interchangeably herein. Unless the context clearly indicates otherwise, neither term implies any limitation on a type of computing system or computing device. In general, a computing system or computing device can be local or distributed, and can include any combination of special-purpose hardware and/or general-purpose hardware with software implementing the functionality described herein.

For the sake of presentation, the detailed description uses terms like “determine” and “use” to describe computer operations in a computing system. These terms are high-level abstractions for operations performed by a computer, and should not be confused with acts performed by a human being. The actual computer operations corresponding to these terms vary depending on implementation.

Example 9 Cloud Computing Environment

FIG. 25 depicts an example cloud computing environment 2500 in which the described technologies can be implemented. The cloud computing environment 2500 comprises cloud computing services 2510. The cloud computing services 2510 can comprise various types of cloud computing resources, such as computer servers, data storage repositories, networking resources, etc. The cloud computing services 2510 can be centrally located (e.g., provided by a data center of a business or organization) or distributed (e.g., provided by various computing resources located at different locations, such as different data centers and/or located in different cities or countries).

The cloud computing services 2510 are utilized by various types of computing devices (e.g., client computing devices), such as computing devices 2520, 2522, and 2524. For example, the computing devices (e.g., 2520, 2522, and 2524) can be computers (e.g., desktop or laptop computers), mobile devices (e.g., tablet computers or smart phones), or other types of computing devices. For example, the computing devices (e.g., 2520, 2522, and 2524) can utilize the cloud computing services 2510 to perform computing operators (e.g., data processing, data storage, and the like).

Example 10 Implementations

Although the operations of some of the disclosed methods are described in a particular, sequential order for convenient presentation, it should be understood that this manner of description encompasses rearrangement, unless a particular ordering is required by specific language set forth below. For example, operations described sequentially may in some cases be rearranged or performed concurrently. Moreover, for the sake of simplicity, the attached figures may not show the various ways in which the disclosed methods can be used in conjunction with other methods.

Any of the disclosed methods can be implemented as computer-executable instructions or a computer program product stored on one or more computer-readable storage media and executed on a computing device (e.g., any available computing device, including smart phones or other mobile devices that include computing hardware). Tangible computer-readable storage media are any available tangible media that can be accessed within a computing environment (e.g., one or more optical media discs such as DVD or CD, volatile memory components (such as DRAM or SRAM), or nonvolatile memory components (such as flash memory or hard drives)). By way of example and with reference to FIG. 24, computer-readable storage media include memory 2420 and 2425, and storage 2440. The term computer-readable storage media does not include signals and carrier waves. In addition, the term computer-readable storage media does not include communication connections (e.g., 2470).

Any of the computer-executable instructions for implementing the disclosed techniques as well as any data created and used during implementation of the disclosed embodiments can be stored on one or more computer-readable storage media. The computer-executable instructions can be part of, for example, a dedicated software application or a software application that is accessed or downloaded via a web browser or other software application (such as a remote computing application). Such software can be executed, for example, on a single local computer (e.g., any suitable commercially available computer) or in a network environment (e.g., via the Internet, a wide-area network, a local-area network, a client-server network (such as a cloud computing network), or other such network) using one or more network computers.

For clarity, only certain selected aspects of the software-based implementations are described. Other details that are well known in the art are omitted. For example, it should be understood that the disclosed technology is not limited to any specific computer language or program. For instance, the disclosed technology can be implemented by software written in C++, Java, Perl, JavaScript, Python, Adobe Flash, or any other suitable programming language. Likewise, the disclosed technology is not limited to any particular computer or type of hardware. Certain details of suitable computers and hardware are well known and need not be set forth in detail in this disclosure.

Furthermore, any of the software-based embodiments (comprising, for example, computer-executable instructions for causing a computer to perform any of the disclosed methods) can be uploaded, downloaded, or remotely accessed through a suitable communication means. Such suitable communication means include, for example, the Internet, the World Wide Web, an intranet, software applications, cable (including fiber optic cable), magnetic communications, electromagnetic communications (including RF, microwave, and infrared communications), electronic communications, or other such communication means.

The disclosed methods, apparatus, and systems should not be construed as limiting in any way. Instead, the present disclosure is directed toward all novel and nonobvious features and aspects of the various disclosed embodiments, alone and in various combinations and sub combinations with one another. The disclosed methods, apparatus, and systems are not limited to any specific aspect or feature or combination thereof, nor do the disclosed embodiments require that any one or more specific advantages be present or problems be solved.

The technologies from any example can be combined with the technologies described in any one or more of the other examples. In view of the many possible embodiments to which the principles of the disclosed technology may be applied, it should be recognized that the illustrated embodiments are examples of the disclosed technology and should not be taken as a limitation on the scope of the disclosed technology. Rather, the scope of the disclosed technology includes what is covered by the scope and spirit of the following claims. 

What is claimed is:
 1. One or more tangible computer-readable storage media storing computer-executable instructions for causing a first database system, the first database system comprising one or more processing units and a memory, for causing the first database system, having such instructions in memory and executed on the one or more processing units, to perform a method for capturing a workload executed on the first database system, the method comprising: receiving a plurality of requests for database operations from one or more database clients; executing the requests for database operations; for the plurality of requests for database operations, generating execution context information for a plurality of execution contexts; for the plurality of requests for database operations, generating a plurality of performance measures; and storing the execution context information and the performance measures in a workload capture file; wherein the workload capture file is useable to replay the captured workload at a second database system using at least a portion of the execution context information and at least a portion of the performance measures.
 2. The one or more tangible computer-readable storage media of claim 1, wherein the execution context information is stored as dimensions, and the performance measures are stored as measures, in a schema.
 3. The one or more tangible computer-readable storage media of claim 2, wherein the schema comprises a star schema.
 4. The one or more tangible computer-readable storage media of claim 1, the method further comprising: generating hash values for at least a portion of the execution context information or the performance measures for each of the plurality of requests for database operations; and storing the hash values with in the workload capture file.
 5. The one or more tangible computer-readable storage media of claim 1, the method further comprising: receiving user input specifying filter criteria for requests for database operations to be included in the workload capture file; and wherein storing the execution context information and performance measures comprises storing execution context information and performance measures for requests for database operations meeting the specified filter criteria.
 6. The one or more tangible computer-readable storage media of claim 1, wherein the database system further comprises a second database server, at least a portion of the requests for database operations being at least partially executable at the second database server, the method further comprising: for at least a portion of the requests for database operations at least partially executable at the second database server, storing in the workload capture file an identifier for the second database server as associated with the at least a portion of the requests at least partially executable at the second database server.
 7. The one or more tangible computer-readable storage media of claim 1, wherein the capture file includes separate files for each of the plurality of execution contexts and at least one file for the performance measures.
 8. The one or more tangible computer-readable storage media of claim 1, the method further comprising: collecting at least one internal database system operation; storing the at least one internal database system operation in the workload capture file.
 9. The one or more tangible computer-readable storage media of claim 1, wherein executing the request for database operations comprises receiving a non-deterministic value for at least one of the requests for database operations and the method further comprises: storing the non-deterministic value in the workload capture file as associated with the at least one of the requests for database operations.
 10. A computing system configured to generate replay data from workload capture data, the computing system comprising: one or more processing units coupled to the memory; and one or more non-transitory computer readable storage media storing instructions that, when loaded into the memory, cause the one or more processing units to perform operations for: receiving a workload capture file, the workload capture file including files for each of a plurality of execution contexts and at least one performance measure file; extracting the plurality of execution context files and the at least one performance measure file to provide execution context data and performance measure data; for each of a plurality of workload capture units, collecting execution context data and performance measure data associated with the respective workload capture unit; for each of the workload capture units, storing the collected execution context data and performance measure data in a format replayable by a second database system.
 11. The computing system of claim 10, wherein extracting the execution context files and the at least one performance measure file comprises decompressing the workload capture file.
 12. The computing system of claim 10, further comprising decompressing the files for each of the plurality of execution contexts and the at least one performance measure file.
 13. The computing system of claim 10, wherein extracting the execution context files comprises extracting a first plurality of the plurality of execution context files in parallel.
 14. The computing system of claim 13, wherein collecting execution context data and performance data associated with the respective workload capture unit comprises carrying out the collecting for a plurality of workload capture units in parallel.
 15. The computing system of claim 14, wherein storing the collected execution context data and performance measure data comprises carrying out the storing for a plurality of workload capture units in parallel.
 16. In a computing system that implements a database environment, the computing system comprising one or more processors and a memory, a method comprising executing by the one or more processors instructions loaded into memory for replaying a captured database workload, the method comprising: receiving workload replay data associated with a workload captured at a first database system, the workload replay data comprising execution context information and performance measures for one or more requests for database operations; executing the one or more requests for database operations at the second database system; generating second performance data associated with the execution of the one or more requests for database operations at the second database system; and comparing first performance data associated with the execution of the captured workload at the first database system and the second performance data.
 17. The method of claim 16, further comprising: verifying execution results from the executing of the one or more requests for database operations at the second database system.
 18. The method of claim 17, wherein the verifying comprises comparing hash values associated with executing the one or more requests for database operations at the second database system with hash values associated with executing the one or more requests for database operations at a first database system, and wherein matching hash values indicate that execution results of the one or more requests for database operations at the second database system match execution results of the one or more requests for database operations at the first database system.
 19. The method of claim 16, wherein the second database system comprises a first node and at least a second node, the method further comprising: sending at least a portion of one of the one or more requests for database operations to the first node; receiving from the first node an identifier for the second node; sending the one of the one or more requests for database operations to the second node for execution; and receiving execution results of the one of the one or more requests for database operations from the second node;
 20. The method of claim 16, wherein the second database system comprises a version of the first database system associated with a first time, the method further comprising: comparing timestamps of the one or more requests for database operations to the first time; and wherein the executing is carried out for requests for database operations of the one or more requests for database operations having a timestamp greater than the first time. 